Table of contents
Quality Control Report
Data
The group variable selected was Sample_Group, and the donor variable was Sample_Name .
Finally, the selected samples added to the RGSet file were:
The following samples were excluded for the analysis:
Intensities boxplots
Green intensities
Red intensities
Failed probes
The probe failure rate is calculated for each sample and two thresholds are represented (5%, 10%).
Normalization options
The selected normalization method was Illumina. In the next plots, we can see the comparison of the processed data with the raw data.
After Minfi normalization, we obtained a GenomicRatioSet with some transformations depending on the type of normalization chosen. To understand better the different classes of Minfi packages, and their relations depending of the normalization method, please read this vignette of Minfi creators (it is also valid for Illumina EPIC arrays).
Options selected:
- Drop SNPs: TRUE minimum MAF filtered: 0
- Drop CpHs: TRUE
- Drop X/Y Chromosomes: FALSE
After normalization, 56049 DNA methylation positions remained.
Density plot
Raw
Processed Illumina
SNPs Heatmap
The methylation array has 65 specific SNP probes. These SNP probes are intended to be used for sample tracking and sample mixups. Each SNP probe ought to have values clustered around 3 distinct values corresponding to homo-, and hetero-zygotes. Therefore, different samples of the same donor should cluster together.
Processed Illumina
Sex Prediction
Depending on the average chromosome Y signal and the average chromosome X signal is possible to predict the sex of the sample donors.
| name | sex |
|---|---|
| 1 | M |
| 2 | M |
| 3 | M |
| 4 | M |
| 5 | M |
| 6 | M |
| 7 | M |
| 8 | M |
| 9 | M |
| 10 | M |
| 11 | M |
| 12 | M |
| 13 | M |
| 14 | M |
| 15 | M |
| 16 | M |
Batch effects
Correlating principal components with variables we can determine if Beta values are related to our variable of interest or other variables. This can also be useful to determine possible errors in the sample hybridization randomization and to select covariables to add to the linear model.
Not useful variables are discarded and the variable type is autodetected. The autodetected variable types were:
System and analysis information
This report was generated at 2021-02-16 17:09:35.
The session information was the following:
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=es_ES.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=es_ES.UTF-8
[6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=es_ES.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0 IlluminaHumanMethylationEPICmanifest_0.3.0
[3] minfi_1.36.0 bumphunter_1.32.0
[5] locfit_1.5-9.4 iterators_1.0.13
[7] foreach_1.5.1 Biostrings_2.58.0
[9] XVector_0.30.0 SummarizedExperiment_1.20.0
[11] Biobase_2.50.0 MatrixGenerics_1.2.1
[13] matrixStats_0.58.0 GenomicRanges_1.42.0
[15] GenomeInfoDb_1.26.2 IRanges_2.24.1
[17] S4Vectors_0.28.1 BiocGenerics_0.36.0
[19] ggplot2_3.3.3 dplyr_1.0.4
[21] shinycssloaders_1.0.0 shinyWidgets_0.5.7
[23] shinydashboardPlus_0.7.5 shinydashboard_0.7.1
[25] shiny_1.6.0
loaded via a namespace (and not attached):
[1] BiocFileCache_1.14.0 plyr_1.8.6 lazyeval_0.2.2 splines_4.0.3 crosstalk_1.1.1
[6] BiocParallel_1.24.1 digest_0.6.27 htmltools_0.5.1.1 rsconnect_0.8.16 viridis_0.5.1
[11] magrittr_2.0.1 memoise_2.0.0 limma_3.46.0 readr_1.4.0 annotate_1.68.0
[16] askpass_1.1 siggenes_1.64.0 prettyunits_1.1.1 colorspace_2.0-0 blob_1.2.1
[21] rappdirs_0.3.3 xfun_0.21 crayon_1.4.1 RCurl_1.98-1.2 jsonlite_1.7.2
[26] genefilter_1.72.1 GEOquery_2.58.0 survival_3.2-7 glue_1.4.2 registry_0.5-1
[31] gtable_0.3.0 zlibbioc_1.36.0 webshot_0.5.2 DelayedArray_0.16.1 Rhdf5lib_1.12.1
[36] HDF5Array_1.18.1 scales_1.1.1 DBI_1.1.1 rngtools_1.5 Rcpp_1.0.6
[41] viridisLite_0.3.0 xtable_1.8-4 progress_1.2.2 bit_4.0.4 mclust_5.4.7
[46] preprocessCore_1.52.1 DT_0.17 htmlwidgets_1.5.3 httr_1.4.2 sourcetools_0.1.7
[51] gplots_3.1.1 RColorBrewer_1.1-2 ellipsis_0.3.1 farver_2.0.3 pkgconfig_2.0.3
[56] reshape_0.8.8 XML_3.99-0.5 sass_0.3.1 dbplyr_2.1.0 labeling_0.4.2
[61] later_1.1.0.1 tidyselect_1.1.0 rlang_0.4.10 reshape2_1.4.4 AnnotationDbi_1.52.0
[66] munsell_0.5.0 tools_4.0.3 cachem_1.0.3 generics_0.1.0 RSQLite_2.2.3
[71] evaluate_0.14 stringr_1.4.0 fastmap_1.1.0 yaml_2.2.1 heatmaply_1.2.1
[76] knitr_1.31 bit64_4.0.5 zip_2.1.1 beanplot_1.2 caTools_1.18.1
[81] scrime_1.3.5 purrr_0.3.4 dendextend_1.14.0 packrat_0.5.0 nlme_3.1-151
[86] doRNG_1.8.2 sparseMatrixStats_1.2.1 mime_0.9 nor1mix_1.3-0 xml2_1.3.2
[91] biomaRt_2.46.3 compiler_4.0.3 rstudioapi_0.13 plotly_4.9.3 curl_4.3
[96] tibble_3.0.6 bslib_0.2.4 stringi_1.5.3 highr_0.8 GenomicFeatures_1.42.1
[101] lattice_0.20-41 Matrix_1.3-2 shinyjs_2.0.0 multtest_2.46.0 vctrs_0.3.6
[106] pillar_1.4.7 lifecycle_0.2.0 rhdf5filters_1.2.0 jquerylib_0.1.3 data.table_1.13.6
[111] bitops_1.0-6 seriation_1.2-9 httpuv_1.5.5 rtracklayer_1.50.0 R6_2.5.0
[116] promises_1.1.1 TSP_1.1-10 KernSmooth_2.23-18 gridExtra_2.3 codetools_0.2-18
[121] MASS_7.3-53 gtools_3.8.2 assertthat_0.2.1 rhdf5_2.34.0 openssl_1.4.3
[126] withr_2.4.1 GenomicAlignments_1.26.0 Rsamtools_2.6.0 GenomeInfoDbData_1.2.4 hms_1.0.0
[131] quadprog_1.5-8 grid_4.0.3 tidyr_1.1.2 base64_2.0 rmarkdown_2.6
[136] DelayedMatrixStats_1.12.3 illuminaio_0.32.0